By and large, an enterprise records purchasing transactions associated with customers for later analysis. These purchasing transactions are sometimes referred to as basket transactions. Typically, although not always, the data associated with a basket transaction is automatically captured in an electronic environment at the time of a sale through automated input devices (e.g., scanners, cash registers, World-Wide Web (WWW) accesses, and the like). In some cases, a basket transaction can be manually collected and later entered into the electronic environment. Furthermore, in some cases, a single basket transaction is automatically captured and later augmented with additional data that is manually entered into an electronic environment.
Today individuals are increasingly purchasing goods and services with credit cards, loyalty cards, and through online transactions over the WWW. Moreover, nearly all goods and services have unique identifiers (e.g., bar codes) that uniquely and automatically identify the type of good or service being sold along with additional information such as department and origin of the good or service being sold. This additional information improves the enterprise's inventory management, planning, and forecasting.
One purpose of recording basket transactions and data related to the basket transactions is to permit an enterprise to more intelligently analyze past sales transactions. The analysis is used to plan and project future sales with the hope that the enterprise can continue doing things that it does well and can improve on things that it may not do well in order to improve sales and profits. One critical aspect in this analysis is to identify different segments of customers that frequent the enterprise and better understand how and why these segments make purchases within the enterprise.
Conventionally, enterprises favor data mining applications as tools for classify large volumes of basket transactions into related segments. The related segments are then viewed for trends or relationships that can assist the enterprise in planning and/or making changes to improve sales.
Conventionally, customer segmentation applications use clustering applications. Clustering applications identify transactions with data that is similar to other transactions and data that is not similar to other transactions. Once these similarities and dissimilarities are discovered then the basket transactions are grouped into similar segments or dissimilar segments. The resulting segments can then be fed to reporting applications (e.g., Online Analytical Processing (OLAP) applications, and others) to generate reports about similar or dissimilar segments.
One of ordinary skill in the art readily appreciates, that clustering applications combined with OLAP reporting applications are not enough for an enterprise to efficiently discern relationships within or between segments. This is so, because OLAP reports are built on predefined or static business requirements. Therefore, the enterprise must have a good idea of what information needs to be reported before developing an OLAP report. Moreover, clustering alone is not good enough for enterprises with high dimensional data, such as when enterprises have many departments and product categories. Also, even if a clustering technique could effectively cluster high dimensional data, the results produced are difficult to interpret. Furthermore, clustering applications combined with a predefined OLAP report will only permit the enterprise to identify and summarize identified segments, but substantial customized report development still needs to occur in order for the enterprise to fully comprehend the effects of the discovered segments on the enterprise.
Therefore, there exist needs for providing techniques that more efficiently and automatically identify customer segmentation from basket transactions. With such techniques, enterprises can more timely and efficiently react and adjust to their environment, customers, and markets.